Interpreting Metabolomic Profiles using Unbiased Pathway Models

Abstract

Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for “active modules”—regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.

New metabolic interdependencies revealed by plasma metabolite profiling after two dietary challenges

“…In accordance with the pathway analysis (Deo et al 2010) our findings demonstrate that the characteristic pattern of amino acid responses to insulin secretion is derived mainly by the various solute transporters with a specificity for groups of amino acids with similar physicochemical characteristics.…”

“…In this context it is interesting to note that plasma glutamine levels slightly increased above baseline under the OGTT while all other system L substrates in plasma decreased. Very recently an almost identical cluster of plasma amino acids was identified in data sets obtained from healthy volunteers undergoing an OGTT by using unbiased pathway models (Deo et al 2010). An interesting finding is that the plasma levels of these ''insulin-sensitive'' amino acids remained decreased even after 180 min despite the fact that glucose levels after the OGTT had returned back to baseline levels already after 90 min.…”

Lipid and Metabolic Changes in Rheumatoid Arthritis

“…There is interest in metabolomics of RA, and both NMR and MS have been successfully used to differentiate RA from other inflammatory diseases and from controls. The next steps will likely involve further integration of RA metabolomics data onto the human metabolic network, such as Recon 2 [98,99]; this integration is seen by some as the greatest challenge in using members of the 'omics' family [100].…”

Metabolic profiling in diabetes

“…One major challenge for the future lies in the integrative interpretation of these datasets, going beyond simple association tests on single phenotypes. New data analysis approaches naturally rely on systems biology methods, including for instance the unbiased analysis of all possible pairs of ratios between metabolite concentrations (Petersen et al 2012), their connection in Gaussian graphical models (Krumsiek et al 2011, and the interpretation of metabolomic profiles using unbiased pathway models (Deo et al 2010). …”